"""Test for short_panel and panel sandwich

Created on Fri May 18 13:05:47 2012

Author: Josef Perktold

moved example from main of random_panel
"""

import numpy as np
from numpy.testing import assert_almost_equal
import numpy.testing as npt
import statsmodels.tools.eval_measures as em
from statsmodels.stats.moment_helpers import cov2corr, se_cov
from statsmodels.regression.linear_model import OLS

from statsmodels.sandbox.panel.panel_short import ShortPanelGLS, ShortPanelGLS2
from statsmodels.sandbox.panel.random_panel import PanelSample
import statsmodels.sandbox.panel.correlation_structures as cs
import statsmodels.stats.sandwich_covariance as sw

def assert_maxabs(actual, expected, value):
    npt.assert_array_less(em.maxabs(actual, expected, None), value)


def test_short_panel():
    #this checks that some basic statistical properties are satisfied by the
    #results, not verified results against other packages
    #Note: the ranking of robust bse is different if within=True
    #I added within keyword to PanelSample to be able to use old example
    #if within is False, then there is no within group variation in exog.
    nobs = 100
    nobs_i = 5
    n_groups = nobs // nobs_i
    k_vars = 3

    dgp = PanelSample(nobs, k_vars, n_groups, corr_structure=cs.corr_arma,
                      corr_args=([1], [1., -0.9],), seed=377769, within=False)
    #print 'seed', dgp.seed
    y = dgp.generate_panel()
    noise = y - dgp.y_true

    #test dgp

    dgp_cov_e = np.array(
              [[ 1.    ,  0.9   ,  0.81  ,  0.729 ,  0.6561],
               [ 0.9   ,  1.    ,  0.9   ,  0.81  ,  0.729 ],
               [ 0.81  ,  0.9   ,  1.    ,  0.9   ,  0.81  ],
               [ 0.729 ,  0.81  ,  0.9   ,  1.    ,  0.9   ],
               [ 0.6561,  0.729 ,  0.81  ,  0.9   ,  1.    ]])

    npt.assert_almost_equal(dgp.cov, dgp_cov_e, 13)

    cov_noise = np.cov(noise.reshape(-1,n_groups, order='F'))
    corr_noise = cov2corr(cov_noise)
    npt.assert_almost_equal(corr_noise, dgp.cov, 1)

    #estimate panel model
    mod2 = ShortPanelGLS(y, dgp.exog, dgp.groups)
    res2 = mod2.fit_iterative(2)


    #whitened residual should be uncorrelated
    corr_wresid = np.corrcoef(res2.wresid.reshape(-1,n_groups, order='F'))
    assert_maxabs(corr_wresid, np.eye(5), 0.1)

    #residual should have same correlation as dgp
    corr_resid = np.corrcoef(res2.resid.reshape(-1,n_groups, order='F'))
    assert_maxabs(corr_resid, dgp.cov, 0.1)

    assert_almost_equal(res2.resid.std(),1, decimal=0)

    y_pred = np.dot(mod2.exog, res2.params)
    assert_almost_equal(res2.fittedvalues, y_pred, 13)


    #compare with OLS

    res2_ols = mod2._fit_ols()
    npt.assert_(mod2.res_pooled is res2_ols)

    res2_ols = mod2.res_pooled  #TODO: BUG: requires call to _fit_ols

    #fitting once is the same as OLS
    #note: I need to create new instance, otherwise it continuous fitting
    mod1 = ShortPanelGLS(y, dgp.exog, dgp.groups)
    res1 = mod1.fit_iterative(1)

    assert_almost_equal(res1.params, res2_ols.params, decimal=13)
    assert_almost_equal(res1.bse, res2_ols.bse, decimal=13)

    res_ols = OLS(y, dgp.exog).fit()
    assert_almost_equal(res1.params, res_ols.params, decimal=13)
    assert_almost_equal(res1.bse, res_ols.bse, decimal=13)


    #compare with old version
    mod_old = ShortPanelGLS2(y, dgp.exog, dgp.groups)
    res_old = mod_old.fit()

    assert_almost_equal(res2.params, res_old.params, decimal=13)
    assert_almost_equal(res2.bse, res_old.bse, decimal=13)


    mod5 = ShortPanelGLS(y, dgp.exog, dgp.groups)
    res5 = mod5.fit_iterative(5)

    #make sure it's different
    #npt.assert_array_less(0.009, em.maxabs(res5.bse, res2.bse))

    cov_clu = sw.cov_cluster(mod2.res_pooled, dgp.groups.astype(int))
    clubse = se_cov(cov_clu)
    pnwbse = se_cov(sw.cov_nw_panel(mod2.res_pooled, 4, mod2.group.groupidx))
    bser = np.vstack((res2.bse, res5.bse, clubse, pnwbse))
    bser_mean = np.mean(bser, axis=0)

    #cov_cluster close to robust and PanelGLS
    #is up to 24% larger than mean of bser
    #npt.assert_array_less(0, clubse / bser_mean - 1)
    npt.assert_array_less(clubse / bser_mean - 1, 0.25)
    #cov_nw_panel close to robust and PanelGLS
    npt.assert_array_less(pnwbse / bser_mean - 1, 0.1)
    #OLS underestimates bse, robust at least 60% larger
    npt.assert_array_less(0.6, bser_mean / res_ols.bse  - 1)

    #cov_hac_panel with uniform_kernel is the same as cov_cluster for balanced
    #panel with full length kernel
    #I fixe default correction to be equal
    cov_uni = sw.cov_nw_panel(mod2.res_pooled, 4, mod2.group.groupidx,
                              weights_func=sw.weights_uniform,
                              use_correction='c')
    assert_almost_equal(cov_uni, cov_clu, decimal=13)

    #without correction
    cov_clu2 = sw.cov_cluster(mod2.res_pooled, dgp.groups.astype(int),
                              use_correction=False)
    cov_uni2 = sw.cov_nw_panel(mod2.res_pooled, 4, mod2.group.groupidx,
                              weights_func=sw.weights_uniform,
                              use_correction=False)
    assert_almost_equal(cov_uni2, cov_clu2, decimal=13)

    cov_white = sw.cov_white_simple(mod2.res_pooled)
    cov_pnw0 = sw.cov_nw_panel(mod2.res_pooled, 0, mod2.group.groupidx,
                              use_correction='hac')
    assert_almost_equal(cov_pnw0, cov_white, decimal=13)
